American Borderlands: Reflections on Margins, Mainstreams, and Alternatives
Bibliographic record
Abstract
Written as the keynote for the 2018 Canadian Association for American Studies (CAAS) conference, this article draws on the author’s personal experience, half-century of historical research, and American art and fiction to examine American mainstreams and alternatives from the boundaries and borderlands of American social relationships and discourses. In the contexts of the Trump administration’s alleged “fake news” and “alternative facts,” it probes who defines the mainstream, who decides what stories are mainstream (or canonical), whose accounts are more “authentic” or “alternative” or just plain lies. Adding marginalized people and movements to history destabilizes “mainstream” histories distorted by skewed sources, silenced stories, and an assumed historical “mainstream” or “consensus.” Marginalized actors push the boundaries of national histories that do not easily accommodate multiple actors or perspectives. “Mainstream” histories of the nation that focus on public politics and powerful actors can make most people appear insignificant and erase the daily acts and grass-roots movements that change the historical mainstream. From unexamined margins, people start or catalyze social change with daily acts that begin to transform social relationships. Changes born in marginalized borderlands can become mainstream truths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.029 | 0.044 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".